{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:S56A6HHEZ23KAG5QPPMXW3A5A3","short_pith_number":"pith:S56A6HHE","canonical_record":{"source":{"id":"2006.07327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-12T17:08:14Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"7d67b291600903ac02b568311a3dc210896f79aa257710630eec62deabaeed98","abstract_canon_sha256":"3a97d1b043b02aaf798297140c3a46e72bf3b6ba642056c2469e4a21aa14d579"},"schema_version":"1.0"},"canonical_sha256":"977c0f1ce4ceb6a01bb07bd97b6c1d06d3e577738e7bde20a8caa5df694d17fa","source":{"kind":"arxiv","id":"2006.07327","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.07327","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"2006.07327v1","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.07327","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"S56A6HHEZ23K","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"pith_short_16","alias_value":"S56A6HHEZ23KAG5Q","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"pith_short_8","alias_value":"S56A6HHE","created_at":"2026-07-05T01:09:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:S56A6HHEZ23KAG5QPPMXW3A5A3","target":"record","payload":{"canonical_record":{"source":{"id":"2006.07327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-12T17:08:14Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"7d67b291600903ac02b568311a3dc210896f79aa257710630eec62deabaeed98","abstract_canon_sha256":"3a97d1b043b02aaf798297140c3a46e72bf3b6ba642056c2469e4a21aa14d579"},"schema_version":"1.0"},"canonical_sha256":"977c0f1ce4ceb6a01bb07bd97b6c1d06d3e577738e7bde20a8caa5df694d17fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:09:53.599548Z","signature_b64":"69kcnpmVGPUuu5ino5S7HCbqU4vGjtQg/ROha60d5Ipz6fIcri+ju4I7xEl/xQoz3WjvaTPDT7sn13YRvjJzBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"977c0f1ce4ceb6a01bb07bd97b6c1d06d3e577738e7bde20a8caa5df694d17fa","last_reissued_at":"2026-07-05T01:09:53.599054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:09:53.599054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.07327","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:09:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i8xDPZ8kV1og5wO/z/aN6EwGUOMAYiZsJMH00eNLzchgfhLDb1vX+5NHXqHUrYiwqIAjq97N+zrlnE5Ib0xVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:29:20.477326Z"},"content_sha256":"b06591ccb625d439a155817aeccbcff8363e41e4ced863e3d51b482934b0972d","schema_version":"1.0","event_id":"sha256:b06591ccb625d439a155817aeccbcff8363e41e4ced863e3d51b482934b0972d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:S56A6HHEZ23KAG5QPPMXW3A5A3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Kris Kitani, Xinshuo Weng, Yongxin Wang, Yunze Man","submitted_at":"2020-06-12T17:08:14Z","abstract_excerpt":"3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses a standard tracking-by-detection pipeline, where feature extraction is first performed independently for each object in order to compute an affinity matrix. Then the affinity matrix is passed to the Hungarian algorithm for data association. A key process of this standard pipeline is to learn discriminative features for different objects in order to reduce confusion during data association. In this work, we propose two techniques to improve the discriminative feature learning for MOT: (1) instead of obtaining feat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.07327","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.07327/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:09:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vx2V3S6DFk0hQKCgv9jJh+0+HUGQmDZUEZ3QXK/tR+dG/zAx7bjFNxnT8KsBkHhoWwgyRb6UEUuNLi09JD1CCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:29:20.477877Z"},"content_sha256":"e7b690fb1eb99f007636e727f1213344b3efd55e943894b394efa4542fdb2da2","schema_version":"1.0","event_id":"sha256:e7b690fb1eb99f007636e727f1213344b3efd55e943894b394efa4542fdb2da2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S56A6HHEZ23KAG5QPPMXW3A5A3/bundle.json","state_url":"https://pith.science/pith/S56A6HHEZ23KAG5QPPMXW3A5A3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S56A6HHEZ23KAG5QPPMXW3A5A3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T13:29:20Z","links":{"resolver":"https://pith.science/pith/S56A6HHEZ23KAG5QPPMXW3A5A3","bundle":"https://pith.science/pith/S56A6HHEZ23KAG5QPPMXW3A5A3/bundle.json","state":"https://pith.science/pith/S56A6HHEZ23KAG5QPPMXW3A5A3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S56A6HHEZ23KAG5QPPMXW3A5A3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:S56A6HHEZ23KAG5QPPMXW3A5A3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3a97d1b043b02aaf798297140c3a46e72bf3b6ba642056c2469e4a21aa14d579","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-12T17:08:14Z","title_canon_sha256":"7d67b291600903ac02b568311a3dc210896f79aa257710630eec62deabaeed98"},"schema_version":"1.0","source":{"id":"2006.07327","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.07327","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"2006.07327v1","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.07327","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"S56A6HHEZ23K","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"pith_short_16","alias_value":"S56A6HHEZ23KAG5Q","created_at":"2026-07-05T01:09:53Z"},{"alias_kind":"pith_short_8","alias_value":"S56A6HHE","created_at":"2026-07-05T01:09:53Z"}],"graph_snapshots":[{"event_id":"sha256:e7b690fb1eb99f007636e727f1213344b3efd55e943894b394efa4542fdb2da2","target":"graph","created_at":"2026-07-05T01:09:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.07327/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses a standard tracking-by-detection pipeline, where feature extraction is first performed independently for each object in order to compute an affinity matrix. Then the affinity matrix is passed to the Hungarian algorithm for data association. A key process of this standard pipeline is to learn discriminative features for different objects in order to reduce confusion during data association. In this work, we propose two techniques to improve the discriminative feature learning for MOT: (1) instead of obtaining feat","authors_text":"Kris Kitani, Xinshuo Weng, Yongxin Wang, Yunze Man","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-12T17:08:14Z","title":"GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.07327","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b06591ccb625d439a155817aeccbcff8363e41e4ced863e3d51b482934b0972d","target":"record","created_at":"2026-07-05T01:09:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3a97d1b043b02aaf798297140c3a46e72bf3b6ba642056c2469e4a21aa14d579","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-12T17:08:14Z","title_canon_sha256":"7d67b291600903ac02b568311a3dc210896f79aa257710630eec62deabaeed98"},"schema_version":"1.0","source":{"id":"2006.07327","kind":"arxiv","version":1}},"canonical_sha256":"977c0f1ce4ceb6a01bb07bd97b6c1d06d3e577738e7bde20a8caa5df694d17fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"977c0f1ce4ceb6a01bb07bd97b6c1d06d3e577738e7bde20a8caa5df694d17fa","first_computed_at":"2026-07-05T01:09:53.599054Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:09:53.599054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"69kcnpmVGPUuu5ino5S7HCbqU4vGjtQg/ROha60d5Ipz6fIcri+ju4I7xEl/xQoz3WjvaTPDT7sn13YRvjJzBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:09:53.599548Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.07327","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b06591ccb625d439a155817aeccbcff8363e41e4ced863e3d51b482934b0972d","sha256:e7b690fb1eb99f007636e727f1213344b3efd55e943894b394efa4542fdb2da2"],"state_sha256":"81e269708fc6b2f5a5a20e2707bd6356bbca5bd4a4d2b95b6dead8dbec1af794"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KTyF6NOWQAxcwi5WX5oPvIoyiOw3+bY0AB7RzWgPV+qi6xgJoYnSipVEc2Kr5OsLoH5qYkNul2zmfYBc2QcJDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T13:29:20.483314Z","bundle_sha256":"46ee8db93c9ca7715e29cf98656d03e155d48f4cf5632d920b8ee0ba1c4746e9"}}